An Evaluation of the Diagnostic Quality of Machine Learning Approaches for PET Attenuation Correction in Neuroimaging: A Meta-Analysis
Confidence Raymond1,2, Jurkiewicz Michael 1,2, Akin Orunmuyi3, Dada Oluwaseun Michael 4, Claes Nøhr Ladefoged5, Jarmo Teuho6, and Udunna Anazodo1,2
1Medical Biophysics, Western University Ontario, London, ON, Canada, 2Lawson Health Research Institute, LONDON, ON, Canada, 3Anaesthesia, College of Medicine, Ibadan, Nigeria, 4Physics, Federal University of Technology, Minna, Nigeria, 5Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Denmark, 6Turku PET Centre, Turku University, Turku, Finland
The last decade has seen an increase in the application of machine learning (ML) methods to PET/MRI attenuation correction (AC). This systematic review provides a head-to-head comparison between state-of-the-art ML methods and clinical standards for AC to determine the clinical feasibility of ML approaches PET AC. We extracted numerical values for image quality, tissue classification, regional and global diagnostic performance. The pooled mean relative error for global performance was 0.87 ± 1.3%, the quality of evidence for all outcomes ranged from moderate to very low. Our findings suggest that ML-AC performance is within acceptable limits for clinical PET/MR neuroimaging.
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